This paper proposes a framework that achieves the Learning from
Observation paradigm for learning dance motions. The framework
enables a humanoid robot to imitate dance motions captured from
human demonstrations. This study especially focuses on leg motions
to achieve a novel attempt in which a biped-type robot imitates not
only upper body motions but also leg motions including steps. Body
differences between the robot and the original dancer make the problem
difficult because the differences prevent the robot from straightforwardly
following the original motions and they also change dynamic
body balance. We propose leg task models, which play a key
role in solving the problem. Low-level tasks in leg motion are modelled
so that they clearly provide essential information required for
keeping dynamic stability and important motion characteristics. The
models divide the problem of adapting motions into the problem of
recognizing a sequence of the tasks and the problem of executing the task sequence. We have developed a method for recognizing the tasks
from captured motion data and a method for generating the motions
of the tasks that can be executed by existing robots including HRP-2.
HRP-2 successfully performed the generated motions, which imitated
a traditional folk dance performed by human dancers.